Paper Strengthening by Starch Xanthate-Alkyl Diepoxide Adducts

The reaction between starch xanthate and alkyl diepoxide formed gels in concentrated solution (6–8%) or turbid suspensions in dilute solution (0.1–1,0%); the gelling varied with the nature of the epoxide. The gel or suspension was dispersed and added to wood pulp suspension and therefrom paper products of improved strength characteristics were formed. Also the reaction was conducted in the presence of paper furnish. Handsheets made from furnish containing a 2.5% additive level based on fiber content had burst, folding endurance, and wet and dry breaking length values 1.6, 4.6, 4.2 and 1.4 times, respectively, those of control papers. Improvement in properties was somewhat independent of the xanthate's degree of substitution but dependent on the reaction time (either separate from or in presence of fiber) and the order of addition of epoxide and xanthate.     

Tailoring Additively Manufactured Titanium Implants for Short-Time Pediatric Implantations with Enhanced Bactericidal Activity

Paediatric titanium (Ti) implants are used for the short-term fixation of fractures, after which they are removed. However, bone overgrowth on the implant surface can complicate their removal. The current Ti implants research focuses on improving their osseointegration and antibacterial properties for long-term use while overlooking the requirements of temporary implants. This paper presents the engineering of additively manufactured Ti implants with antibacterial properties and prevention of bone cell overgrowth. 3D-printed implants were fabricated followed by electrochemical anodization to generate vertically aligned titania nanotubes (TNTs) on the surface with specific diameters (∼100 nm) to reduce cell attachment and proliferation. To achieve enhanced antibacterial performance, TNTs were coated with gallium nitrate as antibacterial agent. The physicochemical characteristics of these implants assessed by the attachment, growth and viability of osteoblastic MG-63 cells showed significantly reduced cell attachment and proliferation, confirming the ability of TNTs surface to avoid cell overgrowth. Gallium coated TNTs showed strong antibacterial activity against S. aureus and P. aeruginosa with reduced bacterial attachment and high rates of bacterial death. Thus a new approach for the engineering of temporary Ti implants with enhanced bactericidal properties with reduced bone cell attachment is demonstrated as a new strategy toward a new generation of short-term implants in paediatrics.     

CFD modeling to predict diffused date syrup yield and quality from sugar production process

In this study, diffusion process of sugar from date is modeled using a commercial computational fluids dynamics (CFD) code FLUENT 6.3.23 (Fluent Inc., USA). A two phases CFD model was developed using an Eulerian–Eulerian approach to calculate the date volume fraction transferred during time from date phase to water phase. The diffusion process was studied as function of three date varieties (Manakher, Lemsi and Alligue), three speeds of agitation (0, 50 and 100 rpm) and three date/water ratio (0.25, 0.50 and 0.75). The results revealed that, for mass transfer, the numerical data were in good agreement with the experimental data indicating the R² of 0.84. Using a Lemsi date variety, the optimal condition of diffusion were 50 rpm and 0.75 for speed of agitation and date/water ratio respectively.     

Design of an optimized traffic-aware routing algorithm using integer linear programming for software-defined networking

The number of internet users and connected devices has dramatically expanded due to the recent technological boom and the benefits that the internet of things offers to ease our lives. Network scheduling, quality of service, resource allocation, and security issues are now being addressed via software-defined networking (SDN). SDN has several benefits over traditional networks, including global centralized control, managing network traffic, and separating the forwarding and control plane. The work done in this paper aims to design and implement a traffic-aware routing framework based on routing optimization presented as an integer linear programming (ILP) to improve heterogeneous traffic flows' quality of service (QoS) in a simulated SDN environment. With the knowledge that the routing problem is a nondeterministic polynomial-time-hard problem, the proposed scheme aims to decrease the computational routing time to make the ILP-based routing system more suitable for real-time processing. The simulation results illustrate that the proposed framework reduces the computational time by 23% and 49% for Abilene and Goodnet topology, respectively. Additionally, with 1000 flows in the network, the suggested scheme reduces the number of network flows that violate the QoS by 9% and 22% (with Abilene topology) and 16% and 51% (with Goodnet topology) as compared to the existing shortest path delay and sway methods, respectively.     

The Polarographic Behaviour of Some Arylhydrazono Azoles

Inspection of the polarographic behaviour of the phenylhydrazones of a variety of heterocyclic azoles having an adjacent functional group indicated that these compounds, unlike typical phenylhydrazones, are reducible in a 4-electron wave in both acidic as well as in alkaline media. A mechanism to explain the electrode processes is proposed.     

An Improved Deep Structure for Accurately Brain Tumor Recognition

Brain neoplasms are recognized with a biopsy, which is not commonly done before decisive brain surgery. By using Convolutional Neural Networks (CNNs) and textural features, the process of diagnosing brain tumors by radiologists would be a noninvasive procedure. This paper proposes a features fusion model that can distinguish between no tumor and brain tumor types via a novel deep learning structure. The proposed model extracts Gray Level Co-occurrence Matrix (GLCM) textural features from MRI brain tumor images. Moreover, a deep neural network (DNN) model has been proposed to select the most salient features from the GLCM. Moreover, it manipulates the extraction of the additional high levels of salient features from a proposed CNN model. Finally, a fusion process has been utilized between these two types of features to form the input layer of additional proposed DNN model which is responsible for the recognition process. Two common datasets have been applied and tested, Br35H and FigShare datasets. The first dataset contains binary labels, while the second one splits the brain tumor into four classes; glioma, meningioma, pituitary, and no cancer. Moreover, several performance metrics have been evaluated from both datasets, including, accuracy, sensitivity, specificity, F-score, and training time. Experimental results show that the proposed methodology has achieved superior performance compared with the current state of art studies. The proposed system has achieved about 98.22% accuracy value in the case of the Br35H dataset however, an accuracy of 98.01% has been achieved in the case of the FigShare dataset.